code: def __init__(
      self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0
  ):
      super().__init__()
  
      self.rnn = nn.LSTM(
          input_size=d_feat,
          hidden_size=hidden_size,
          num_layers=num_layers,
          batch_first=True,
          dropout=dropout,
      )
      self.fc_out = nn.Linear(hidden_size, 1)
  
      self.d_feat = d_feat
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from ...model.base import Model
  from ...data.dataset.handler import DataHandlerLP
  from ...model.utils import ConcatDataset
  from ...data.dataset.weight import Reweighter
label_desc: Use image node_id 1 to create a new LSTMModel object from inherited base classes: nn with example: obj = LSTMModel(d_feat, hidden_size, num_layers, dropout)